






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase__      			=						logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

UpperCAmelCase__      			=						list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase__      			=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 							snake_case_       :

						"""simple docstring"""



						snake_case__        =      field(
						    default="""cifar10"""		,							metadata={"""help""": """Name of a dataset from the datasets package"""}      )
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}		,							)
						snake_case__        =      field(default=lowerCamelCase__		,							metadata={"""help""": """A folder containing the training data."""}      )
						snake_case__        =      field(default=lowerCamelCase__		,							metadata={"""help""": """A folder containing the validation data."""}      )
						snake_case__        =      field(
						    default=0.15		,							metadata={"""help""": """Percent to split off of train for validation."""}      )
						snake_case__        =      field(default=32		,							metadata={"""help""": """The size of the square patches to use for masking."""}      )
						snake_case__        =      field(
						    default=0.6		,							metadata={"""help""": """Percentage of patches to mask."""}		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of training examples to this """
						            """value if set."""
						        )
						    }		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
						            """value if set."""
						        )
						    }		,							)



						def        UpperCAmelCase__					(self:					List[Any]   )    ->     int:




											'''simple docstring'''



											__a					:						Any      =					{}
											if self.train_dir is not None:
																__a					:						Dict      =					self.train_dir
											if self.validation_dir is not None:
																__a					:						Union[str, Any]      =					self.validation_dir
											__a					:						Any      =					data_files if data_files else None




@dataclass
class 							snake_case_       :

						"""simple docstring"""



						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={
						        """help""": (
						            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
						            """checkpoint identifier on the hub. """
						            """Don't set if you want to train a model from scratch."""
						        )
						    }		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCamelCase__      )}		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={
						        """help""": (
						            """Override some existing default config settings when a model is trained from scratch. Example: """
						            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
						        )
						    }		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}		,							)
						snake_case__        =      field(
						    default="""main"""		,							metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,							)
						snake_case__        =      field(default=lowerCamelCase__		,							metadata={"""help""": """Name or path of preprocessor config."""}      )
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={
						        """help""": (
						            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
						            """with private models)."""
						        )
						    }		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={
						        """help""": (
						            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
						        )
						    }		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={
						        """help""": (
						            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
						        )
						    }		,							)
						snake_case__        =      field(
						    default=lowerCamelCase__		,							metadata={"""help""": """Stride to use for the encoder."""}		,							)




class 							snake_case_       :

						"""simple docstring"""



						def __init__(self:					List[str] ,					__UpperCAmelCase:					int=192 ,					__UpperCAmelCase:					int=32 ,					__UpperCAmelCase:					List[str]=4 ,					__UpperCAmelCase:					Union[str, Any]=0.6   )    ->     Any:




											'''simple docstring'''



											__a					:						Any      =					input_size
											__a					:						Union[str, Any]      =					mask_patch_size
											__a					:						int      =					model_patch_size
											__a					:						Tuple      =					mask_ratio

											if self.input_size % self.mask_patch_size != 0:
																raise ValueError("Input size must be divisible by mask patch size"   )
											if self.mask_patch_size % self.model_patch_size != 0:
																raise ValueError("Mask patch size must be divisible by model patch size"   )

											__a					:						str      =					self.input_size // self.mask_patch_size
											__a					:						Union[str, Any]      =					self.mask_patch_size // self.model_patch_size

											__a					:						str      =					self.rand_size**2
											__a					:						Tuple      =					int(np.ceil(self.token_count * self.mask_ratio   )   )



						def __call__(self:					str   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						List[str]      =					np.random.permutation(self.token_count   )[: self.mask_count]
											__a					:						List[str]      =					np.zeros(self.token_count ,					dtype=__snake_case   )
											__a					:						Any      =					1

											__a					:						List[str]      =					mask.reshape((self.rand_size, self.rand_size)   )
											__a					:						Tuple      =					mask.repeat(self.scale ,					axis=0   ).repeat(self.scale ,					axis=1   )

											return torch.tensor(mask.flatten()   )







def     a_   (__A      )				->						Optional[int]:



					"""simple docstring"""
					__a					:						str      =					torch.stack([example["pixel_values"] for example in examples]      )
					__a					:						List[str]      =					torch.stack([example["mask"] for example in examples]      )
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def     a_   ()				->						int:



					"""simple docstring"""
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					__a					:						int      =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
					if len(sys.argv      ) == 2 and sys.argv[1].endswith(".json"      ):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										__a					:						Dict      =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
					else:
										__a					:						int      =					parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry("run_mim"				,       _A				,       _A      )

					# Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,       datefmt="%m/%d/%Y %H:%M:%S"				,       handlers=[logging.StreamHandler(sys.stdout      )]				,       )

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					__a					:						int      =					training_args.get_process_log_level()
					logger.setLevel(_A      )
					transformers.utils.logging.set_verbosity(_A      )
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
					    + f'distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}'      )
					logger.info(f'Training/evaluation parameters {training_args}'      )

					# Detecting last checkpoint.
					__a					:						Tuple      =					None
					if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
										__a					:						List[Any]      =					get_last_checkpoint(training_args.output_dir      )
										if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
															raise ValueError(
															    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
															    "Use --overwrite_output_dir to overcome."      )
										elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
															logger.info(
															    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
															    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."      )

										# Initialize our dataset.
					__a					:						Any      =					load_dataset(
					    data_args.dataset_name				,       data_args.dataset_config_name				,       data_files=data_args.data_files				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )

					# If we don't have a validation split, split off a percentage of train as validation.
					__a					:						Union[str, Any]      =					None if 'validation' in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split				,       _A      ) and data_args.train_val_split > 0.0:
										__a					:						Any      =					ds['train'].train_test_split(data_args.train_val_split      )
										__a					:						str      =					split['train']
										__a					:						Any      =					split['test']

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					__a					:						Tuple      =					{
					    'cache_dir': model_args.cache_dir,
					    'revision': model_args.model_revision,
					    'use_auth_token': True if model_args.use_auth_token else None,
					}
					if model_args.config_name_or_path:
										__a					:						Any      =					AutoConfig.from_pretrained(model_args.config_name_or_path				,       **_A      )
					elif model_args.model_name_or_path:
										__a					:						Optional[int]      =					AutoConfig.from_pretrained(model_args.model_name_or_path				,       **_A      )
					else:
										__a					:						Optional[int]      =					CONFIG_MAPPING[model_args.model_type]()
										logger.warning("You are instantiating a new config instance from scratch."      )
										if model_args.config_overrides is not None:
															logger.info(f'Overriding config: {model_args.config_overrides}'      )
															config.update_from_string(model_args.config_overrides      )
															logger.info(f'New config: {config}'      )

										# make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(_A				,       "decoder_type"      ):
										__a					:						List[str]      =					'simmim'

					# adapt config
					__a					:						str      =					model_args.image_size if model_args.image_size is not None else config.image_size
					__a					:						Union[str, Any]      =					model_args.patch_size if model_args.patch_size is not None else config.patch_size
					__a					:						Any      =					(
					    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
					)

					config.update(
					    {
					        "image_size": model_args.image_size,
					        "patch_size": model_args.patch_size,
					        "encoder_stride": model_args.encoder_stride,
					    }      )

					# create image processor
					if model_args.image_processor_name:
										__a					:						Union[str, Any]      =					AutoImageProcessor.from_pretrained(model_args.image_processor_name				,       **_A      )
					elif model_args.model_name_or_path:
										__a					:						Union[str, Any]      =					AutoImageProcessor.from_pretrained(model_args.model_name_or_path				,       **_A      )
					else:
										__a					:						List[Any]      =					{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										__a					:						int      =					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										__a					:						Tuple      =					AutoModelForMaskedImageModeling.from_pretrained(
										    model_args.model_name_or_path				,       from_tf=bool(".ckpt" in model_args.model_name_or_path      )				,       config=_A				,       cache_dir=model_args.cache_dir				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       )
					else:
										logger.info("Training new model from scratch"      )
										__a					:						Union[str, Any]      =					AutoModelForMaskedImageModeling.from_config(_A      )

					if training_args.do_train:
										__a					:						Tuple      =					ds['train'].column_names
					else:
										__a					:						Dict      =					ds['validation'].column_names

					if data_args.image_column_name is not None:
										__a					:						Optional[Any]      =					data_args.image_column_name
					elif "image" in column_names:
										__a					:						str      =					'image'
					elif "img" in column_names:
										__a					:						Union[str, Any]      =					'img'
					else:
										__a					:						str      =					column_names[0]

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					__a					:						Optional[int]      =					Compose(
					    [
					        Lambda(lambda __A      : img.convert("RGB"      ) if img.mode != "RGB" else img      ),
					        RandomResizedCrop(model_args.image_size				,       scale=(0.67, 1.0)				,       ratio=(3.0 / 4.0, 4.0 / 3.0)      ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean				,       std=image_processor.image_std      ),
					    ]      )

					# create mask generator
					__a					:						Dict      =					MaskGenerator(
					    input_size=model_args.image_size				,       mask_patch_size=data_args.mask_patch_size				,       model_patch_size=model_args.patch_size				,       mask_ratio=data_args.mask_ratio				,       )

					def preprocess_images(__A      ):
										__a					:						Dict      =					[transforms(_A      ) for image in examples[image_column_name]]
										__a					:						Optional[int]      =					[mask_generator() for i in range(len(examples[image_column_name]      )      )]

										return examples

					if training_args.do_train:
										if "train" not in ds:
															raise ValueError("--do_train requires a train dataset"      )
										if data_args.max_train_samples is not None:
															__a					:						List[Any]      =					ds['train'].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
										# Set the training transforms
										ds["train"].set_transform(_A      )

					if training_args.do_eval:
										if "validation" not in ds:
															raise ValueError("--do_eval requires a validation dataset"      )
										if data_args.max_eval_samples is not None:
															__a					:						str      =					(
															    ds['validation'].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
															)
										# Set the validation transforms
										ds["validation"].set_transform(_A      )

					# Initialize our trainer
					__a					:						List[str]      =					Trainer(
					    model=_A				,       args=_A				,       train_dataset=ds["train"] if training_args.do_train else None				,       eval_dataset=ds["validation"] if training_args.do_eval else None				,       tokenizer=_A				,       data_collator=_A				,       )

					# Training
					if training_args.do_train:
										__a					:						str      =					None
										if training_args.resume_from_checkpoint is not None:
															__a					:						Any      =					training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__a					:						Dict      =					last_checkpoint
										__a					:						Optional[int]      =					trainer.train(resume_from_checkpoint=_A      )
										trainer.save_model()
										trainer.log_metrics("train"				,       train_result.metrics      )
										trainer.save_metrics("train"				,       train_result.metrics      )
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										__a					:						List[Any]      =					trainer.evaluate()
										trainer.log_metrics("eval"				,       _A      )
										trainer.save_metrics("eval"				,       _A      )

					# Write model card and (optionally) push to hub
					__a					:						str      =					{
					    'finetuned_from': model_args.model_name_or_path,
					    'tasks': 'masked-image-modeling',
					    'dataset': data_args.dataset_name,
					    'tags': ['masked-image-modeling'],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**_A      )
					else:
										trainer.create_model_card(**_A      )


if __name__ == "__main__":
			main()










'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )